Hopping transport in the space-charge region of p-n structures with InGaN/GaN QWs as a source of excess 1/f noise and efficiency droop in LEDs

Semiconductors - It is shown that the emission efficiency and the 1/f noise level in light-emitting diodes with InGaN/GaN quantum wells correlate with how the differential resistance of a diode...     

Hopping conductivity and dielectric relaxation in Schottky barriers on GaN

A study of the current and capacitance dependences on the forward voltage in Au/n-GaN Schottky diodes, the sub-band optical absorption spectra, and the defect photoluminescence in n-GaN bulk crystals and thin layers is reported. It is shown that defect-assisted tunneling is the dominant transport mechanism for forward-biased Schottky contacts on n-GaN. The dependences of the current and capacitance on forward bias reflect the energy spectrum of defects in the band gap of n-GaN: the rise in the density of deep states responsible for yellow photoluminescence in GaN with increasing energy and the steep exponential tail of states with an Urbach energy of E _U = 50 meV near the conduction-band edge. A decrease in the frequency of electron hops near the Au/n-GaN interface results in a wide distribution of local dielectric relaxation times and in a dramatic transformation of the electric-field distribution in the space-charge region under forward biases.     

On the laser detachment of <Emphasis Type="Italic">n</Emphasis>-GaN films from substrates,based on the strong absorption of IR light by free charge carriers in <Emphasis Type="Italic">n</Emphasis> <Superscript>+</Superscript>-GaN substrates

The physical and technological basics of the method used to lift off lightly and moderately doped n-GaN films from heavily doped n⁺-GaN substrates are considered. The detachment method is based on the free-charge-carrier absorption of IR laser light, which is substantially higher in n⁺-GaN films.     

Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival

Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta-analysis of such datasets is, however, very complicated for various reasons. Here, we developed an integrating statistical and machine-learning model approach for the meta-analysis of bisphenol A (BPA) exposure datasets from different mouse tissues. We constructed three joint datasets following three different strategies for dataset integration: in particular, using all common genes from the datasets, uncorrelated, and not co-expressed genes, respectively. By applying machine learning methods to these datasets, we identified genes whose expression was significantly affected in all of the BPA microanalysis data tested; those involved in the regulation of cell survival include: Tnfr2, Hgf-Met, Agtr1a, Bdkrb2; signaling through Mapk8 (Jnk1)); DNA repair (Hgf-Met, Mgmt); apoptosis (Tmbim6, Bcl2, Apaf1); and cellular junctions (F11r, Cldnd1, Ctnd1 and Yes1). Our results highlight the benefit of combining existing datasets for the integrated analysis of a specific topic when individual datasets are limited in size.